Image Forgery Detection with Machine Learning

<p>The issue of forged images is currently a global issue that spreads mainly via social networks. Image forgery has weakened Internet users’ confidence in digital images. In recent years, extensive research has been devoted to the development of new techniques to combat various image forgery attacks. Detecting fake images prevents counterfeit photos from being used to deceive or cause harm to others. In this thesis, we propose methods using the error level analysis algorithm to detect manipulated images. We show that our combination of image pre-processing and machine learning techniques is an efficient approach to detecting image forgery attacks.</p>

Name thesis:degree_name

Master of Science (MS)

Discipline thesis:degree_discipline

Computational Science

Year dc:date.available

2020

Author dc:creator

• Alzamil, Lubna

Contributors dc:contributor

• Razvan Andonie
• Szilard Vajda
• Boris Kovalerchuk

• photo manipulation
• social media
• social networks
• influence campaigns
• deepfake
• detection
• Computer Engineering

Repository record dc:identifier

https://digitalcommons.cwu.edu/etd/1361

OAI identifier oai:identifier

oai:digitalcommons.cwu.edu:etd-2385